CN110689266A - Land potential studying and judging method based on multi-source data - Google Patents
Land potential studying and judging method based on multi-source data Download PDFInfo
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Abstract
The invention discloses a land potential studying and judging method based on multi-source data, which comprises the following specific steps of: s1: establishing a comprehensive evaluation system for the area to be researched and judged, and determining an evaluation index; s2: the evaluation indexes are spatialized, and the areas to be researched and judged are equally divided into grid units with uniform sizes according to the evaluation indexes; s3: selecting n grid units as judging data samples X; s4: appointing k cluster, initializing k cluster centers; s5: calculating Euclidean clustering from each grid unit to each clustering center, sequentially comparing the distance from each grid unit to each clustering center, and distributing the grid units to the cluster of the clustering center closest to the grid unit to obtain k clusters; s6: and performing Kmeans clustering in a space range on the spatialization indexes, and judging the land potential level of each land in the area to be researched. The method can be used for clearly predicting the land use potential of each land in the area to be researched and judged, and provides great help for urban planning.
Description
Technical Field
The invention relates to the field of quantitative evaluation of land use potential, in particular to a land use potential studying and judging method based on multi-source data.
Background
The land potential research and judgment belongs to the field of land evaluation research, is a spatial evaluation research, and firstly needs to consider three problems during the research and judgment: selecting an evaluation unit, selecting an evaluation index and applying an evaluation method. The evaluation unit is selected to demarcate the space unit with the minimum land evaluation, the space unit needs to have certain homogeneity, and meanwhile, the space units in the evaluation range can have the same index acquirability, so that unified evaluation is facilitated. The evaluation indexes represent the angles of land evaluation, the indexes mainly have scale potential, economic benefit, location potential and the like, the indexes of each angle can comprise a plurality of factors, land potential research and judgment is a multi-factor evaluation problem, and the selected indexes can better reflect core problems and can be actually obtained and need to be considered; the evaluation method is characterized in that the evaluation unit in the research range is subjected to global analysis based on the evaluation indexes, the evaluation method is comprehensive, related research is concentrated on several traditional land evaluation methods, mainly including a principal component analysis method, a Telfiy method, an analytic hierarchy process, a regression analysis method and a discriminant analysis method, the evaluation methods are statistical analysis of the land evaluation indexes, and the land itself is less considered to have spatial similarity and neighbor property.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a land use potential studying and judging method based on multi-source data, which can be used for preparing and predicting the land use potential of the land to be studied and judged.
In order to achieve the purpose, the invention adopts the following technical scheme:
a land potential studying and judging method based on multi-source data comprises the following specific steps:
s1: establishing a comprehensive evaluation system for the area to be researched and judged, and determining an evaluation index;
s2: the evaluation indexes are spatialized, and the areas to be researched and judged are equally divided into grid units with uniform sizes according to the evaluation indexes;
s3: selecting n grid units as judging data samples X;
s4: appointing k clusters according to the area control area and the control requirement, and initializing k cluster centers;
s5: calculating Euclidean clustering from each grid unit to each clustering center, sequentially comparing the distance from each grid unit to each clustering center, and distributing the grid units to the cluster of the clustering center closest to the grid unit to obtain k clusters;
s6: and performing Kmeans clustering in a space range on the spatialized indexes, and judging the land use potential level of each land in the area to be judged according to the specific positive characteristics of each index.
Further, the specific method for building the comprehensive evaluation system in step S1 is as follows: dividing the area to be researched into five categories of residence, commute, work, rest and policy;
different evaluation indexes are respectively set in each class.
Further, in the step S3, the grid cells each have attributes of m dimensions.
Further, in the step S5, the grid cell belongs to and only belongs to one cluster with the smallest distance to the center of the cluster.
The invention has the beneficial effects that: the land utilization potential of each land in the area to be researched and judged can be clearly predicted by the research and judgment method, the land utilization potential of each level land in the area can be clearly predicted by the prediction, and great help is provided for city planning.
Drawings
FIG. 1 is a flow chart of a judging method of the present invention;
FIG. 2 is a diagram of planning potential index preprocessing.
Detailed Description
The invention will be further described with reference to the accompanying drawings and the detailed description below:
the method for researching and judging the land potential is more, wherein a market comparison method adopts a fuzzy evaluation method, has higher market adaptability, can deal with the condition of land price and object price fluctuation in the process of urban rapid development, is simple, convenient and rapid in calculation process, and meets the comprehensive requirements on efficiency and accuracy. The market analogy method comprises the steps of carrying out analogy on the natural and social attributes of all developable lands to find out groups with similar attributes, then utilizing the actual development benefits of the known similar land development to judge the group potential, and analogizing the potential of the land to be developed,
the market analogy method includes the steps of conducting analogy on natural and social attributes of all developable lands, finding out groups with similar attributes, conducting group judgment by utilizing the potential of the known similar land development, and analogizing the potential of the land to be developed. The method mainly comprises three aspects, namely, firstly, dividing an evaluation range into grid units with uniform size uniformly to ensure the uniformity of the evaluation units; secondly, the space has forest proximity similar to similarity, and the adjacent land without indexes can obtain evaluation indexes through means such as difference values; and thirdly, the space classification algorithm has more classical space classification algorithms in remote sensing classification research, and is often used for a long time for reference.
Kmeans is a classical unsupervised classification method, similarity measurement can be carried out on expected development potentials of all places in a research area, and the subjectivity of a traditional evaluation method for weight evaluation of all factors is avoided. Therefore, market analogy of the potential of each plot is realized, and the evaluation level of the development potential of each plot is obtained.
The Kmeans algorithm is the most common clustering algorithm, and the main algorithm idea is as follows: under the condition of giving K values and K initial cluster center points, each point (namely data record) is divided into the cluster represented by the cluster center point closest to the point, after all the points are distributed, the cluster center point is recalculated (averaged) according to all the points in one cluster, and then the steps of distributing the points and updating the cluster center point are iterated until the change of the cluster center point is small or the appointed iteration times are reached.
Example one
As shown in fig. 1, a land potential studying and judging method based on multi-source data includes the following specific steps:
s1: establishing a comprehensive evaluation system for the area to be researched and judged, and determining an evaluation index;
dividing the area to be researched and judged into five categories of residence, commuting, work, rest and policy, and setting two evaluation indexes in each category; as shown in the following table:
s2: the evaluation indexes are spatialized, and the areas to be researched and judged are equally divided into grid units with uniform sizes according to the evaluation indexes;
s3: selecting n grid units as judging data samples X;
given a grid unit sample X, n grid units X ═ X are included1,X2,X3,…,Xn-wherein each grid cell has attributes of m dimensions;
s4: appointing k clusters according to the area control area and the control requirement, and initializing k cluster centers;
the objective of the Kmeans algorithm is to group the n grid cells into k designated class clusters according to the similarity between the grid cells, and each object belongs to and only belongs to one class cluster with the minimum distance from the center of the class cluster. First, k cluster centers C need to be initialized1,C2,C3,…,Ck},1<k is less than or equal to n, then by calculating each grid cell to each cluster centerEuclidean distance, as shown in the following equation:
in the above formula, XiDenotes the ith object (1. ltoreq. i. ltoreq.n), CjRepresents the jth cluster center (1 ≦ j ≦ k), XitThe t-th attribute (1. ltoreq. t. ltoreq.m), C, representing the ith objectjtThe t-th attribute representing the j-th cluster center.
S5: calculating Euclidean clustering from each grid unit to each clustering center, sequentially comparing the distance from each grid unit to each clustering center, and distributing the grid units to the cluster of the nearest clustering center to obtain k clusters { S }1,S2,S3,…,Sk}; the Kmeans algorithm defines a prototype of a class cluster by using a center, wherein the class cluster center is the mean value of all objects in the class cluster in each dimension, and the calculation formula is as follows:
in the formula, ClRepresents the center of the first cluster (1. ltoreq. l. ltoreq.k), count (S)l) Indicates the number of grid cells in the ith class cluster, XiRepresents the ith object in the ith class cluster, i is more than or equal to 1 and less than or equal to count (S)l)。
S6: and performing Kmeans clustering in a space range on the spatialized indexes, and judging the land use potential level of each land in the area to be judged according to the specific positive characteristics of each index.
Fig. 2 is a diagram of a planning potential indicator preprocessing.
Performing Kmeans clustering in a spatial range on the spatialization indexes to obtain 6 types of results, wherein the higher the potential grade is, the greater the potential is, and the following table is a land prediction development benefit statistical table:
| potential rating | Area of | Ratio of |
| 6 | 1420.99 hectare | 7.07％ |
| 5 | 2063.82 hectare | 10.27％ |
| 4 | 5122.06 hectare | 25.49％ |
| 3 | 7219.07 hectare | 35.92％ |
| 2 | 4144.76 hectare | 20.63％ |
| 1 | 124.59 hectare | 0.62％ |
| Total area of | 20095.29 hectare | - |
Through a potential clustering result graph, the potential of the land use is found to be in a clear circle layer shape in a central area in the whole city range and in a clear radial shape along rivers and roads.
The red plots with the potential grade of 6 are relatively outstanding, are distributed in the center and the edge of the area, are key development areas in recent planning, have the highest potential, have the area of 1420.99 hectares and account for 7.07 percent of the whole area; the potential grade of 5 is in the core zone of the research area, namely the area with concentrated distribution of traffic, public uniforms, population and patents, the infrastructure and public service of the area are the most perfect, the traffic condition is good, the population is dense, the current state of land has higher environmental value, the area is 2063.82 hectare, and the proportion of the area in the whole research area is 10.27%; the land with the potential grade of 4 is positioned in a secondary peripheral area and in a first circle layer radiated in a central area, the current land has a secondary high environmental value, and the area percentage is 25.49%; the land with the potential level of 3 is in an area which is not developed near the edge of a mountain and close to a river, the current state value of the land is low, the land can be developed after 'seven-pass and one-level' is needed, the development cost is high, the area accounts for 35.92%, the land is the land which is distributed in the widest whole area, the areas with the potential levels of 2 and 1 are the areas with the lowest current state value, the areas are basically river and forest coverage areas, ecological core zones are mostly prohibited to be built, and the area accounts for 21.25%.
Various other modifications and changes may be made by those skilled in the art based on the above-described technical solutions and concepts, and all such modifications and changes should fall within the scope of the claims of the present invention.
Claims (4)
1. A land potential studying and judging method based on multi-source data is characterized by comprising the following specific steps:
s1: establishing a comprehensive evaluation system for the area to be researched and judged, and determining an evaluation index;
s2: the evaluation indexes are spatialized, and the areas to be researched and judged are equally divided into grid units with uniform sizes according to the evaluation indexes;
s3: selecting n grid units as judging data samples X;
s4: appointing k cluster, initializing k cluster centers;
s5: calculating Euclidean clustering from each grid unit to each clustering center, sequentially comparing the distance from each grid unit to each clustering center, and distributing the grid units to the cluster of the clustering center closest to the grid unit to obtain k clusters;
s6: and performing Kmeans clustering in a space range on the spatialization indexes, and judging the land potential level of each land in the area to be researched.
2. The land use potential studying and judging method based on multi-source data as claimed in claim 1, wherein the concrete method for building the comprehensive evaluation system in the step S1 is as follows: dividing the area to be researched into five categories of residence, commute, work, rest and policy;
different evaluation indexes are respectively set in each class.
3. The land use potential judging method based on multi-source data as claimed in claim 1, wherein in step S3, the grid cells have m-dimensional attributes.
4. The method for judging land use potential based on multi-source data according to claim 1, wherein in the step S5, the grid cell belongs to and only belongs to one cluster with the smallest distance to the center of the cluster.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036721A (en) * | 2020-08-21 | 2020-12-04 | 洛阳众智软件科技股份有限公司 | GDAL-based land resource development double-evaluation method, device and equipment |
| CN113869438A (en) * | 2021-09-30 | 2021-12-31 | 自然资源部第三海洋研究所 | Key defense area dividing method for storm surge disaster based on k-means |
| CN115511253A (en) * | 2022-08-05 | 2022-12-23 | 广州市城市规划勘测设计研究院 | Method, device, equipment and medium for evaluating block development capacity |
| CN118246715A (en) * | 2024-05-29 | 2024-06-25 | 山东省国土空间生态修复中心(山东省地质灾害防治技术指导中心、山东省土地储备中心) | Intelligent selection method and system for urban and rural construction land |
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- 2019-09-27 CN CN201910925112.0A patent/CN110689266A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112036721A (en) * | 2020-08-21 | 2020-12-04 | 洛阳众智软件科技股份有限公司 | GDAL-based land resource development double-evaluation method, device and equipment |
| CN113869438A (en) * | 2021-09-30 | 2021-12-31 | 自然资源部第三海洋研究所 | Key defense area dividing method for storm surge disaster based on k-means |
| CN113869438B (en) * | 2021-09-30 | 2023-04-07 | 自然资源部第三海洋研究所 | Key defense area dividing method for storm surge disaster based on k-means |
| CN115511253A (en) * | 2022-08-05 | 2022-12-23 | 广州市城市规划勘测设计研究院 | Method, device, equipment and medium for evaluating block development capacity |
| CN115511253B (en) * | 2022-08-05 | 2023-12-29 | 广州市城市规划勘测设计研究院 | Method, device, equipment and medium for evaluating land development capacity |
| CN118246715A (en) * | 2024-05-29 | 2024-06-25 | 山东省国土空间生态修复中心(山东省地质灾害防治技术指导中心、山东省土地储备中心) | Intelligent selection method and system for urban and rural construction land |
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Application publication date: 20200114 |